{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Linear Regression Data Exploration: Lending Club\n",
    "=========\n",
    "***\n",
    "\n",
    "### How can I predict interest rates based on borrower and loan attributes?\n",
    "\n",
    "The [Lending Club](http://www.lendingclub.com) is a peer-to-peer lending site where members make loans to each other.\n",
    "The site makes anonymized data on loans and borrowers publicly available.  We're going to use these data to explore how the interest rate charged on loans depends on various factors.\n",
    "\n",
    "We want to explore these data, try to gain some insights into what might be useful in creating a linear regression model, and to separate out \"the noise\".\n",
    "\n",
    "We follow these steps, something we will do in future for other data sets as well.\n",
    "\n",
    "1. Browse the data  \n",
    "2. Data cleanup  \n",
    "3. Visual exploration  \n",
    "4. Model derivation  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## I. Browse Data\n",
    "\t\t \t \t\n",
    "The data have the following variables (with data type and explanation of meaning)\n",
    "\t\t\t\n",
    "* __Amount.Requested__ - _numeric_. The amount (in dollars) requested in the loan application.  \t\t\n",
    "  \n",
    "* __Amount.Funded.By.Investors__ - _numeric_.  The amount (in dollars) loaned to the individual.  \n",
    "\n",
    "* Interest.rate – character. The lending interest rate charged to the borrower.  \n",
    "\n",
    "* Loan.length - character. The length of time (in months) of the loan.    \t\t\t\n",
    "\n",
    "* Loan.Purpose – categorical variable. The purpose of the loan as stated by the applicant.   \n",
    "\n",
    "* Debt.to.Income.Ratio – character  The % of consumer’s gross income going toward paying debts.   \n",
    "\n",
    "* State - character. The abbreviation for the U.S. state of residence of the loan applicant.   \n",
    "\n",
    "* Home.ownership - character. Indicates whether the applicant owns, rents, or has a mortgage.   \n",
    "\n",
    "* Monthly.income -­ categorical. The monthly income of the applicant (in dollars).  \n",
    "\n",
    "* FICO.range  – categorical (expressed as a string label e.g. “650-655”). A range indicating the applicants FICO score. \t\n",
    "\n",
    "* Open.CREDIT.Lines - numeric. The number of open lines of credit at the time of application.  \n",
    "\n",
    "* Revolving.CREDIT.Balance - numeric. The total amount outstanding all lines of credit.   \t\t\t\n",
    "\n",
    "* Inquiries.in.the.Last.6.Months - numeric. Number of credit inquiries in the previous 6 months.  \n",
    "\n",
    "* Employment.Length - character.  Length of time employed at current job.  \n",
    "\t\t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## II. Data Cleanup \n",
    "\n",
    "We find the data are \"messy\" i.e aren't cleanly prepared for import - for instance numeric columns might have some strings in them.  This is very common in raw data especially that obtained from web sites.\n",
    "\n",
    "Let's take a look. we're going to look at the first five rows of some specific columns that show the data dirtiness issues."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "# first we ingest the data from the source on the web\n",
    "# this contains a reduced version of the data set from Lending Club\n",
    "import pandas as pd\n",
    "loansData = pd.read_csv('https://spark-public.s3.amazonaws.com/dataanalysis/loansData.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81174     8.90%\n",
       "99592    12.12%\n",
       "80059    21.98%\n",
       "15825     9.99%\n",
       "33182    11.71%\n",
       "Name: Interest.Rate, dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loansData['Interest.Rate'][0:5] # first five rows of Interest.Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81174    36 months\n",
       "99592    36 months\n",
       "80059    60 months\n",
       "15825    36 months\n",
       "33182    36 months\n",
       "Name: Loan.Length, dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loansData['Loan.Length'][0:5] # first five rows of Loan.Length"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see here that:\n",
    "\n",
    "* the interest rate information has \"%\" symbols in it.\n",
    "* loan length has \" months\" in it\n",
    "\n",
    "Other than that we can also see (exploration exercise):\n",
    "\n",
    "* there are a couple of values that are so large they must be typos\n",
    "* some values are missing \"NA\" values i.e. not available.\n",
    "* the FICO Range is really a numeric entity but is represented as a categorical variable in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81174    735-739\n",
       "99592    715-719\n",
       "80059    690-694\n",
       "15825    695-699\n",
       "33182    695-699\n",
       "Name: FICO.Range, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loansData['FICO.Range'][0:5] # first five rows of FICO.Range"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "FICO Range is represented as a categorical variable in the data.\n",
    "\n",
    "We need to change the categorical variable for FICO Range into something numeric so that we can use it in our calculations.  As it stands, the values are merely labels, and while they convey meaning to humans, our software can't interpret them as the numbers they really represent.\n",
    "\n",
    "So as a first step, we convert them from categorical variables to strings.  So the abstract entity 735-739 becomes a string \"735-739\".\n",
    "Then we parse the strings so that a range such as \"735-739\" gets split into two numbers (735,739).\n",
    "\n",
    "Finally we pick a single number to represent this range. We could choose a midpoint but since the ranges are narrow we can get away with choosing one of the endpoints as a representative. Here we arbitrarily pick the lower limit and with some imperious hand waving, assert that it is not going to make a major difference to the outcome.\n",
    "\n",
    "In a further flourish of imperiousness we could declare that \"the proof is left as an exercise to the reader\". But in reality there is really no such formal \"proof\" other than trying it out in different ways and convincing oneself.  If we wanted to be mathematically conservative we could take the midpoint of the range as a representative and this would satisfy most pointy-haired mathematician bosses that \"Data Science Dilbert\" might encounter."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "To summarize - cleaning our data involves:\n",
    "\n",
    "* removing % signs from rates\n",
    "* removing the word ” months\" from loan length.\n",
    "* managing outliers - remove such rows in this case\n",
    "* managing NA - remove such rows in this case\n",
    "\n",
    "There is one especially high outlier with monthly income > 100K$+.  \n",
    "This is likely to be a typo and is removed as a data item.  \n",
    "There is also one data item with all N/A - this is also removed.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Exercise\n",
    "Actually perform each of the above steps on the dataset i.e.\n",
    "\n",
    "* import the data\n",
    "* remove the '%' suffix from each row\n",
    "* remove the ' months' suffix from each row\n",
    "* remove the outlier rows\n",
    "* remove rows with NA\n",
    "\n",
    "Save your code in a reusable manner - these are steps you'll be doing repeatedly.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Visual Exploration  \n",
    "\n",
    "Now we are going to follow a standard set of steps in exploring data.  We apply the following simple visualizations. This is something we will typically also do for other data sets we encounter in other explorations.  \n",
    "\n",
    "###Histogram\n",
    "\n",
    "A histogram shows us the shape of the distribution of values for a **single** variable.\n",
    "On the x-axis we have the variable under question, divided into buckets or bins.  This is a key feature of a histogram.\n",
    "\n",
    "The bin size is adjustable and different bin sizes give different information.  A large bin size gives us an idea of the coarser grained structure of the distribution while a smaller bin size will shine light on the finer details of the distribution.  In either case we can compare distributions, or quickly identify some key hints that tell use how best to proceed.\n",
    "\n",
    "With the distribution of FICO scores we see the histogram below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a282ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "plt.figure()\n",
    "loansmin = pd.read_csv('../datasets/loanf.csv')\n",
    "fico = loansmin['FICO.Score']\n",
    "p = fico.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  \n",
    "\n",
    "Why do we look at FICO score?  Because we know from domain knowledge that this is the primary determinant of interest rate.  \n",
    "The histogram shows us that the distribution is not a normal or gaussian distribution but that there are some other factors that might be affecting or distorting the shape of the distribution away from the bell curve.  We want to dig a little deeper.  \n",
    "\n",
    "###Box Plot\n",
    "\n",
    "Next we take a box plot which allows us to quickly look at the distribution of interest rates based on each FICO score range.   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10222d790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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m9tZE9lR1Zh+j6Bq9Dz69y45WmnmNUUxrCG1uw1pzzPWU8hx5jKKPNjCY/Shg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\nOI7jOK38D/8HJXm9GsSbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10223f4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "plt.figure()\n",
    "loansmin = pd.read_csv('../datasets/loanf.csv')\n",
    "\n",
    "p = loansmin.boxplot('Interest.Rate','FICO.Score')\n",
    "q = p.set_xticklabels(['640','','','','660','','','','680','','','','700',\n",
    "  '720','','','','740','','','','760','','','','780','','','','800','','','','820','','','','840'])\n",
    "\n",
    "q0 = p.set_xlabel('FICO Score')\n",
    "q1 = p.set_ylabel('Interest Rate %')\n",
    "q2 = p.set_title('                          ')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First of all this tells us that there is a general downward trend in interest rate for higher FICO scores.\n",
    "But, given the same range of FICO scores we see a range of interest rates not a single value - so it appears there are other factors determining interest rate, given the same FICO score range. We want to investigate the impact of these other drivers and quantify this impact.  \n",
    "\n",
    "What might these be?    \n",
    "\n",
    "Let's use a little domain knowledge again. We know interest rate is based on risk to the borrower: the greater the risk, the greater the interest rate charged to compensate for the risk.  Another factor that might affect risk is the size of the loan - the larger the amount the greater the risk of non-payment and also the greater the negative impact of actual default.\n",
    "\n",
    "We want to look at multiple factors and how they might affect the interest rate.  \n",
    "A great way to look at multiple factors simultaneously is the scatterplot matrix.  We are going to use this as the next step in visual exploration.  \n",
    "\n",
    "### Scatterplot Matrix\n",
    "\n",
    "But first what is it?  \n",
    "\n",
    "The scatterplot matrix is a grid of plots of multiple variables against each other.  It shows the relationship of each variable to the others.  The ones on the diagonal don't fit this pattern. Why not? What does it mean to find the relationship of something to itself, in this context.  Not much, since we are trying to determine the impact of some variable on **another** variable.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're going to look at a scatterplot matrix of the five variables in our data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"files/images/important.png\" />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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t4VU1KBTavma2bbO6KvPHZTIGwWDSi26Wz0s7KbCmaRiGwfo6aJqKZc1z40aT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HZGrK07YaD/dupzK5JMhgHo7e3qyeCzDbA1boa73c7yso6mwdJSCUGwmJ0dRpKWqFZlLCuGLNdQ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OTtbN78FmS56VIk9bSZb0Q4foAB/qnhWkOlfwS8Bvw4wmD6FPD/Ah+7ijEcoAJscv+OA2V3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\n2hqksHyj9KPAj0TkF75+7QL+pUX9cvraS6uuw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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a282890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## TRY THIS!\n",
    "import pandas as pd\n",
    "loansmin = pd.read_csv('../datasets/loanf.csv')\n",
    "a = pd.scatter_matrix(loansmin,alpha=0.05,figsize=(10,10), diagonal='hist')\n",
    "## Click on the line above\n",
    "## Change 'hist' to 'kde' then hit shift-enter, with the cursor still in this box\n",
    "## The plot will redraw - it takes a while. While it is recomputing you will see a \n",
    "## message-box that says 'Kernel Busy' near the top right corner\n",
    "## You can change the code and hit shift-enter to re-execute the code\n",
    "## Try changing the (10,10) to (8,8) and (12,12)\n",
    "## Try changing the alpha value from 0.05 to 0.5 \n",
    "## How does this change in alpha change your ability to interpret the data?\n",
    "## Feel free to try other variations. \n",
    "## If at any time you scramble the code and forget the syntax \n",
    "## a copy of the original code is below. Copy and paste it in place. \n",
    "## Remember to remove the hashmarks.\n",
    "## a = pd.scatter_matrix(loansmin, alpha=0.05,figsize=(10,10), diagonal='hist)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this diagram, the boxes on the diagonal contain histogram plots of the respective variable itself.\n",
    "\n",
    "So if the 3rd variable is Loan Amount then the third row and third column are the Loan Amount column and row. And the third element down the diagonal is the histogram of the Loan Amount.  \n",
    "\n",
    "To see how Loan Amount (3rd) affects Interest Rate (1st) then we look for the intersection of the 3rd row and the 1st column.  \n",
    "\n",
    "We also notice that we could have looked for the intersection of the 3rd column and 1st row.  They have the same plot. The scatterplot matrix plot is visually symmetric about the diagonal.\n",
    "\n",
    "Where there is some significant, useful effect we will see a noticeable trend in the scatterplot at the intersection.  Where there is none we will see no noticeable trend.  \n",
    "\n",
    "What do the last two sentences mean in practice?\n",
    "\n",
    "Let's compare two plots: the first one at the intersection of 1st row and 2nd column, and the second at the intersection of 1st row 4th column.\n",
    "\n",
    "In the first, FICO score shows an approximate but unmistakeable linear trend.\n",
    "\n",
    "In the second, Monthly Income shows no impact as we move along the x-axis. All the dots are bunched up near one end but show no clear, linear trend like the first one. \n",
    "\n",
    "Similarly there is no obvious variation in the plot for Loan Length while there is a distinct but increasing trend trend also in the plot for Loan Amount.\n",
    "\n",
    "So what does this suggest?  It suggests that we should use FICO and Loan Amount in our model as independent variables, while Monthly Income and Loan Length don't seem to be too useful as independent variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "So at the end of this data alchemy exercise we have distilled our variables into two beakers - one has what we believe is relevant - the data nuggets, and the other, the data dross.....the variables that have no visible impact on our dependent variable.\n",
    "\n",
    "We're going to refine our output even further into a model in the next step - the analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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     "metadata": {},
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   ],
   "source": [
    "from IPython.core.display import HTML\n",
    "def css_styling():\n",
    "    styles = open(\"../styles/custom.css\", \"r\").read()\n",
    "    return HTML(styles)\n",
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   "cell_type": "code",
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